Multi-objective reinforcement learning-based disassembly line balancing optimization method, system, and medium

A multi-objective reinforcement learning-based disassembly line balancing optimization system, comprising:

• an acquisition unit configured to obtain input data and perform preprocessing by obtaining various types of data during a task execution process of a disassembly task and performing preprocessing, recording a disassembly precedence constraint, constructing a disassembly precedence constraint matrix, obtaining a hazardous property as well as a demand property of the disassembly task, and obtaining a disassembly time, a disassembly cost, and a disassembly energy consumption of different disassembly tasks on a manual workstation and a robotic workstation, respectively;
• a construction unit configured to construct a state space, an action space, and a reward function, and deploy a training environment, wherein the state space comprises a plurality of state features, and the state features comprise at least one of the following states:
• a workstation load state, a robot load state, a task assignment state, a predecessor task completion state, a successor task heuristic state, a task-station fitting state, and a cycle offset state;
• and wherein the action space comprises a plurality of action policies, and the action policies comprise at least one of the following policies:
• a longest processing time (LPT) first policy, a shortest processing time (SPT) first policy, a least predecessors (LP) first policy, a most successors (MS) first policy, an optimal cycle time matching (OCTM) policy, and a random policy;
• and further wherein an objective function is designed based on three objectives of a number of workstations, the disassembly cost, and the disassembly energy consumption, and a multi-objective reinforcement learning model is constructed based on a multi-objective deep Q-network algorithm (MO-DQN algorithm);
• the objective function is denoted as:
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• w
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• wherein w1, w2, and w3 are weight coefficients of various objectives and reflect a relative importance of the various objectives in decision-making to achieve a collaborative optimization among different objectives, f1 denotes the number of workstations, f2 denotes the disassembly cost, and f3 denotes the disassembly energy consumption;
• a training unit configured to train the multi-objective reinforcement learning model, wherein the multi-objective reinforcement learning model introduces a preference vector through the MO-DQN algorithm to dynamically adjust objective weights, calculates the objective function based on the disassembly data obtained by the acquisition unit, and optimizes a plurality of objectives defined by the construction unit;
• and wherein the training unit selects an action policy by combining an E-greedy strategy and a non-dominated sorting genetic algorithm II (NSGA-II), and finally converges to an optimal model capable of balancing the plurality of objectives;
• and further wherein during a training process, the MO-DQN algorithm combines a plurality of objective values to form a Q-value function in a vector form, and uses different preference weights as conditional inputs during the training process to learn a generalization mapping from preferences to Q-values, and the NSGA-II performs non-dominated sorting on a multi-objective Q-value vector corresponding to an action and calculates a crowding distance to determine an optimal action;
• and
• an optimization unit configured to perform task scheduling and resource allocation optimization by applying an optimal policy generated by the multi-objective reinforcement learning model that is trained to task assignment in an actual disassembly line.